sealed trait BinaryLogisticRegressionSummary extends LogisticRegressionSummary
:: Experimental :: Abstraction for binary logistic regression results for a given model.
Currently, the summary ignores the instance weights.
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- @Experimental()
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abstract
def
featuresCol: String
Field in "predictions" which gives the features of each instance as a vector.
Field in "predictions" which gives the features of each instance as a vector.
- Definition Classes
- LogisticRegressionSummary
- Annotations
- @Since( "1.6.0" )
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abstract
def
labelCol: String
Field in "predictions" which gives the true label of each instance (if available).
Field in "predictions" which gives the true label of each instance (if available).
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- LogisticRegressionSummary
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- @Since( "1.5.0" )
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abstract
def
predictionCol: String
Field in "predictions" which gives the prediction of each class.
Field in "predictions" which gives the prediction of each class.
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- LogisticRegressionSummary
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- @Since( "2.3.0" )
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abstract
def
predictions: DataFrame
Dataframe output by the model's
transform
method.Dataframe output by the model's
transform
method.- Definition Classes
- LogisticRegressionSummary
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- @Since( "1.5.0" )
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abstract
def
probabilityCol: String
Field in "predictions" which gives the probability of each class as a vector.
Field in "predictions" which gives the probability of each class as a vector.
- Definition Classes
- LogisticRegressionSummary
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- @Since( "1.5.0" )
Concrete Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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def
accuracy: Double
Returns accuracy.
Returns accuracy. (equals to the total number of correctly classified instances out of the total number of instances.)
- Definition Classes
- LogisticRegressionSummary
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- @Since( "2.3.0" )
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lazy val
areaUnderROC: Double
Computes the area under the receiver operating characteristic (ROC) curve.
Computes the area under the receiver operating characteristic (ROC) curve.
- Annotations
- @Since( "1.5.0" )
- Note
This ignores instance weights (setting all to 1.0) from
LogisticRegression.weightCol
. This will change in later Spark versions.
-
def
asBinary: BinaryLogisticRegressionSummary
Convenient method for casting to binary logistic regression summary.
Convenient method for casting to binary logistic regression summary. This method will throw an Exception if the summary is not a binary summary.
- Definition Classes
- LogisticRegressionSummary
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- @Since( "2.3.0" )
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
fMeasureByLabel: Array[Double]
Returns f1-measure for each label (category).
Returns f1-measure for each label (category).
- Definition Classes
- LogisticRegressionSummary
- Annotations
- @Since( "2.3.0" )
-
def
fMeasureByLabel(beta: Double): Array[Double]
Returns f-measure for each label (category).
Returns f-measure for each label (category).
- Definition Classes
- LogisticRegressionSummary
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- @Since( "2.3.0" )
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lazy val
fMeasureByThreshold: DataFrame
Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.
Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.
- Annotations
- @Since( "1.5.0" ) @transient()
- Note
This ignores instance weights (setting all to 1.0) from
LogisticRegression.weightCol
. This will change in later Spark versions.
-
def
falsePositiveRateByLabel: Array[Double]
Returns false positive rate for each label (category).
Returns false positive rate for each label (category).
- Definition Classes
- LogisticRegressionSummary
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- @Since( "2.3.0" )
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def
finalize(): Unit
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def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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def
labels: Array[Double]
Returns the sequence of labels in ascending order.
Returns the sequence of labels in ascending order. This order matches the order used in metrics which are specified as arrays over labels, e.g., truePositiveRateByLabel.
Note: In most cases, it will be values {0.0, 1.0, ..., numClasses-1}, However, if the training set is missing a label, then all of the arrays over labels (e.g., from truePositiveRateByLabel) will be of length numClasses-1 instead of the expected numClasses.
- Definition Classes
- LogisticRegressionSummary
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- @Since( "2.3.0" )
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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lazy val
pr: DataFrame
Returns the precision-recall curve, which is a Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.
Returns the precision-recall curve, which is a Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.
- Annotations
- @Since( "1.5.0" ) @transient()
- Note
This ignores instance weights (setting all to 1.0) from
LogisticRegression.weightCol
. This will change in later Spark versions.
-
def
precisionByLabel: Array[Double]
Returns precision for each label (category).
Returns precision for each label (category).
- Definition Classes
- LogisticRegressionSummary
- Annotations
- @Since( "2.3.0" )
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lazy val
precisionByThreshold: DataFrame
Returns a dataframe with two fields (threshold, precision) curve.
Returns a dataframe with two fields (threshold, precision) curve. Every possible probability obtained in transforming the dataset are used as thresholds used in calculating the precision.
- Annotations
- @Since( "1.5.0" ) @transient()
- Note
This ignores instance weights (setting all to 1.0) from
LogisticRegression.weightCol
. This will change in later Spark versions.
-
def
recallByLabel: Array[Double]
Returns recall for each label (category).
Returns recall for each label (category).
- Definition Classes
- LogisticRegressionSummary
- Annotations
- @Since( "2.3.0" )
-
lazy val
recallByThreshold: DataFrame
Returns a dataframe with two fields (threshold, recall) curve.
Returns a dataframe with two fields (threshold, recall) curve. Every possible probability obtained in transforming the dataset are used as thresholds used in calculating the recall.
- Annotations
- @Since( "1.5.0" ) @transient()
- Note
This ignores instance weights (setting all to 1.0) from
LogisticRegression.weightCol
. This will change in later Spark versions.
-
lazy val
roc: DataFrame
Returns the receiver operating characteristic (ROC) curve, which is a Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
Returns the receiver operating characteristic (ROC) curve, which is a Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it. See http://en.wikipedia.org/wiki/Receiver_operating_characteristic
- Annotations
- @Since( "1.5.0" ) @transient()
- Note
This ignores instance weights (setting all to 1.0) from
LogisticRegression.weightCol
. This will change in later Spark versions.
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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def
truePositiveRateByLabel: Array[Double]
Returns true positive rate for each label (category).
Returns true positive rate for each label (category).
- Definition Classes
- LogisticRegressionSummary
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- @Since( "2.3.0" )
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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def
weightedFMeasure: Double
Returns weighted averaged f1-measure.
Returns weighted averaged f1-measure.
- Definition Classes
- LogisticRegressionSummary
- Annotations
- @Since( "2.3.0" )
-
def
weightedFMeasure(beta: Double): Double
Returns weighted averaged f-measure.
Returns weighted averaged f-measure.
- Definition Classes
- LogisticRegressionSummary
- Annotations
- @Since( "2.3.0" )
-
def
weightedFalsePositiveRate: Double
Returns weighted false positive rate.
Returns weighted false positive rate.
- Definition Classes
- LogisticRegressionSummary
- Annotations
- @Since( "2.3.0" )
-
def
weightedPrecision: Double
Returns weighted averaged precision.
Returns weighted averaged precision.
- Definition Classes
- LogisticRegressionSummary
- Annotations
- @Since( "2.3.0" )
-
def
weightedRecall: Double
Returns weighted averaged recall.
Returns weighted averaged recall. (equals to precision, recall and f-measure)
- Definition Classes
- LogisticRegressionSummary
- Annotations
- @Since( "2.3.0" )
-
def
weightedTruePositiveRate: Double
Returns weighted true positive rate.
Returns weighted true positive rate. (equals to precision, recall and f-measure)
- Definition Classes
- LogisticRegressionSummary
- Annotations
- @Since( "2.3.0" )